Take a different approach with metabolites:
Here include genotype for the treatment models but use scaled and centered values for leaf associations.
NOTE: THIS IS THE FINAL ANALYSIS. TRIED AND DID NOT LIKE LME MODELS. TOO MANY SINGULARITIES. I THINK THERE IS NOT ENOUGH DATA TO ESTIMATE BLOCK EFFECCTS WELL.
NOTE: NEXT STEPS: TRY MAKING TERNARY PLOT WITH SIMPLE MEANS.
library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.8
✓ tidyr 1.2.0 ✓ stringr 1.4.0
✓ readr 2.1.2 ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(broom)get leaflength data
leaflength <- read_csv("../../plant/output/leaf_lengths_metabolite.csv") %>%
mutate(pot=str_pad(pot, width=3, pad="0"),
sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
select(sampleID, genotype, trt, leaf_avg_std)Rows: 36 Columns: 6
── Column specification ─────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): soil, genotype, trt
dbl (3): pot, leaf_avg, leaf_avg_std
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.leaflength %>% arrange(sampleID)get and wrangle metabolite data
met_raw <-read_csv("../input/metabolites_set1.csv")Rows: 72 Columns: 671
── Column specification ─────────────────────────────────────────────────────────────────
Delimiter: ","
chr (6): tissue, soil, genotype, autoclave, time_point, concatenate
dbl (665): submission_number, pot, sample_mass mg, xylulose NIST, xylose, xylonic aci...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.met <- met_raw %>%
mutate(pot=str_pad(pot, width = 3, pad = "0")) %>%
mutate(sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
mutate(trt=ifelse(is.na(autoclave), "BLANK", autoclave)) %>%
select(sampleID, genotype, tissue, trt, sample_mass = `sample_mass mg`, !submission_number:concatenate) %>%
#bring in leaf length
left_join({leaflength %>% select(sampleID, leaf_avg_std)}) %>%
select(sampleID, genotype, tissue, trt, leaf_avg_std, everything()) %>%
#make long
pivot_longer(!sampleID:sample_mass, names_to = "metabolite", values_to = "met_amt") %>%
#filter away unnamed
filter(str_detect(metabolite, pattern="^[0-9]+$", negate=TRUE)) %>%
#adjust by sample mass
mutate(met_per_mg=met_amt/sample_mass) %>%
pivot_wider(id_cols = c(sampleID, genotype, trt, leaf_avg_std),
names_from = c(tissue, metabolite),
values_from = starts_with("met_"),
names_sep = "_")Joining, by = "sampleID"met split this into two data frames, one normalized by tissue amount and one not.
met_per_mg <- met %>% select(sampleID, genotype, trt, leaf_avg_std, starts_with("met_per_mg")) %>%
as.data.frame() %>% column_to_rownames("sampleID")
met_amt <- met %>% select(sampleID, genotype, trt, leaf_avg_std, starts_with("met_amt")) %>%
as.data.frame() %>% column_to_rownames("sampleID")Live vs blank dead
normalized
met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
unnest(broomtidy.trt) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
select(-`p.value_trt_(Intercept)`)
met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
unnest(broomtidy.leaf) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
select(-`p.value_leaf_(Intercept)`)
met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
group_by(tissue) %>%
mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
ungroup()Joining, by = "metabolite"met_per_mg_lm_results %>%
mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())
met_per_mg_lm_results %>%
mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())
met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)
met_per_mg_lm_results %>%
#filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>%
write_csv("../output/met_per_mg_lm_full.csv")raw
met_amt_lm <- met_amt %>%
mutate(trt=ifelse(str_detect(trt, "live"), "live", "blank_dead")) %>%
pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
mutate(tissue=str_extract(metabolite, "leaf|root")) %>%
#scale and center
group_by(metabolite, genotype, tissue) %>%
mutate(value_scale=scale(value)) %>%
group_by(metabolite) %>%
nest() %>%
mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
unnest(broomtidy.trt) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
select(-`p.value_trt_(Intercept)`)
met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
unnest(broomtidy.leaf) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
select(-`p.value_leaf_(Intercept)`)
met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
group_by(tissue) %>%
mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
ungroup()Joining, by = "metabolite"met_amt_lm_results %>%
mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())
met_amt_lm_results %>%
mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())
met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)
met_amt_lm_results %>%
#filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>%
write_csv("../output/met_amt_lm_full.csv")Live vs dead
normalized
```r
met_per_mg_lm <- met_per_mg %>%
filter(trt != \BLANK\) %>%
mutate(trt=ifelse(str_detect(trt, \live\), \live\, \blank_dead\)) %>%
pivot_longer(cols=starts_with(\met\), names_to = \metabolite\) %>%
mutate(tissue=str_extract(metabolite, \leaf|root\)) %>%
#scale and center
group_by(metabolite, genotype, tissue) %>%
mutate(value_scale=scale(value)) %>%
group_by(metabolite) %>%
nest() %>%
mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))
<!-- rnb-source-end -->
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```r
```r
met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
unnest(broomtidy.trt) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \trt_\) %>%
select(-`p.value_trt_(Intercept)`)
met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
unnest(broomtidy.leaf) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \leaf_\) %>%
select(-`p.value_leaf_(Intercept)`)
met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
mutate(tissue=str_extract(metabolite,\(leaf|root)\)) %>%
group_by(tissue) %>%
mutate(across(starts_with(\p.value\), .fns = p.adjust, method = \fdr\, .names = \FDR_{.col}\)) %>%
rename_with(~ str_replace(.x, \FDR_p.value\, \FDR\)) %>%
ungroup()
<!-- rnb-source-end -->
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Joining, by =
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```r
```r
met_per_mg_lm_results %>%
mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with(\FDR\), ~ sum(.x < .1)), both=sum(both), count=n())
met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)
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```r
```r
met_per_mg_lm_results %>%
#filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>%
write_csv(\../output/met_per_mg_lm_full_NOBLANK.csv\)
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raw
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```r
```r
met_amt_lm <- met_amt %>%
filter(trt != \BLANK\) %>%
pivot_longer(cols=starts_with(\met\), names_to = \metabolite\) %>%
mutate(tissue=str_extract(metabolite, \leaf|root\)) %>%
#scale and center
group_by(metabolite, genotype, tissue) %>%
mutate(value_scale=scale(value)) %>%
group_by(metabolite) %>%
nest() %>%
mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))
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```r
```r
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
unnest(broomtidy.trt) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \trt_\) %>%
select(-`p.value_trt_(Intercept)`)
met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
unnest(broomtidy.leaf) %>%
select(metabolite, term, estimate, p.value) %>%
pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \leaf_\) %>%
select(-`p.value_leaf_(Intercept)`)
met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
mutate(tissue=str_extract(metabolite,\(leaf|root)\)) %>%
group_by(tissue) %>%
mutate(across(starts_with(\p.value\), .fns = p.adjust, method = \fdr\, .names = \FDR_{.col}\)) %>%
rename_with(~ str_replace(.x, \FDR_p.value\, \FDR\)) %>%
ungroup()
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Joining, by =
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```r
```r
met_amt_lm_results %>%
mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
group_by(tissue) %>%
summarize(across(starts_with(\FDR\), ~ sum(.x < .1)), both=sum(both), count=n())
met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)
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```r
```r
met_amt_lm_results %>%
#filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>%
write_csv(\../output/met_amt_lm_full_NOBLANK.csv\)```